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Record W2743057838 · doi:10.1590/0034-7167-2016-0633

Frailty in the elderly: prevalence and associated factors

2017· article· en· W2743057838 on OpenAlexaboutno aff
Jair Almeida Carneiro, Rafael Cardoso, Meiriellen Silva Durães, Maria Clara Araújo Guedes, Frederico Leão Santos, Fernanda Marques da Costa, Antônio Prates Caldeira

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPoisson regressionMedicineGerontologySocioeconomic statusElderly peopleLogistic regressionDemographyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Objective: to know the prevalence and factors associated with frailty in elderly assisted by the Centro Mais Vida de Referência em Assistência à Saúde do Idoso (Mais Vida Health Reference Center for the Elderly) in the North of Minas Gerais, Brazil. Method: cross-sectional study, with sampling by convenience. Data collection occurred in 2015. Demographic and socioeconomic variables, morbidities, use of health services and the score of the Edmonton Frail Scale were analyzed. The adjusted prevalence ratios were obtained by multiple analysis of Poisson regression with robust variance. Results: 360 elderly aged 65 or older were evaluated. Frailty prevalence was 47.2%. The variables associated with frailty were the following: advanced age elderly, who live without a partner, have a caregiver, present depressive symptoms, osteoarticular disease, as well as history of hospitalization and falls in the last twelve months. Conclusion: knowledge of factors associated with frailty allows development of health actions aimed at the elderly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.348
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations147
Published2017
Admission routes1
Has abstractyes

Explore more

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